1. Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning
- Author
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Fudan Zheng, Huiling Zhu, Zhiguang Chen, Liang Li, Ziwang Huang, Xiang Zhang, Jiahao Wu, Yutian Chong, Ying Song, Yuedong Yang, Yutong Lu, Yunfei Zha, Weifeng Chen, Huiying Zhao, and Jun Shen
- Subjects
2019-20 coronavirus outbreak ,medicine.medical_specialty ,Diagnostic methods ,Coronavirus disease 2019 (COVID-19) ,Pneumonia, Viral ,Health Informatics ,Computed tomography ,General Biochemistry, Genetics and Molecular Biology ,Diagnosis, Differential ,03 medical and health sciences ,Deep Learning ,Pneumonia classifying ,Predictive Value of Tests ,Multidetector Computed Tomography ,medicine ,Pneumonia, Bacterial ,Humans ,Original Research Article ,Diagnosis, Computer-Assisted ,Lung ,030304 developmental biology ,0303 health sciences ,medicine.diagnostic_test ,business.industry ,Deep learning ,030302 biochemistry & molecular biology ,Bacterial pneumonia ,COVID-19 ,Reproducibility of Results ,medicine.disease ,Image diagnosis ,Computer Science Applications ,Deep learning network ,CT image ,Viral pneumonia ,Case-Control Studies ,Diagnosis of COVID-19 ,Radiographic Image Interpretation, Computer-Assisted ,Artificial intelligence ,Radiology ,business - Abstract
Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification. Graphic Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s12539-021-00420-z. more...
- Published
- 2021